Multiple sclerosis is a neurodegenerative disease that affects over 2.3 million people around the world. This disease is characterized by inflammatory demyelination, which occurs when the body's immune system attacks the protective myelin sheath that covers the nerves and aids in sending electrical signals. To demonstrate correlations between lesions and clinical symptoms, it is important to be able to characterize the lesions of a patient in a quantitative and objective measure. Many studies use a lesion load metric; however, lesion segmentation is typically a subjective and time-consuming task.
While MRI has been used to identify myelin features in the nervous system, most efforts to characterize myelin involve visual inspection of MRI scans by expert radiologists. This is a time-consuming, expensive, and error-prone process, subject to several subjective biases, not least that humans are notoriously poor at simultaneously assessing statistical relationships between more than two or three variables. A natural tendency is to focus on gross boundaries and local textures. When considering multimodal images, this problem is multiplied several-fold because in order to digest all the available evidence, the analyst has to assess, pixel-by-pixel, the local environment in as many as four distinct modalities. Typically, this forces the analyst to concentrate on only one modality, with the “best” contrast for a particular tissue, and disregard potential contrary evidence in the other modalities. Classification accuracy is subject to variability between researchers and even for the same researcher over time, making a standardized diagnostic test virtually impossible. In most cases, validation of the interpreted image can only be accomplished by histological examination of endarterectomies. Given these importance of myelin detection and analysis to patient health, there is a clear need for improved methods for the detection and analysis of myelin in vivo.
There is weak correlation between lesion volume segmented from T1W and T2W images in diseases like multiple sclerosis. This is in part due to the omission of the degree of injury associated with lesions; i.e. the degree of demyelination or axonal loss. Advanced imaging modalities (including diffusion tensor MRI, magnetization transfer MRI, T1 and T2 relaxation times maps) can provide this information but are technically more challenging to implement and compute and are generally not included in standard imaging protocols. Using the ratio of T2 and T1 scans provides a quantitative metric of demyelination based on the combined T1 and T2 relaxation times without proton density weighting from readily available images in almost all clinical and standard MRI protocols. Furthermore, this ratio diminishes the confounding effects of bias fields on the images.
The ratio of T2 and T1 scans can be used in a novel way as a metric for demyelination in the central nervous system (brain and spinal cord) and degree of damage from myelin-associated disorders, e.g. Siemerling-Creutzfeldt disease, autoimmune disorders, neurological disorders, traumatic injury, or age-related degeneration. By thresholding these maps, methods presented herein can identify which areas are lesions and create a quantified metric to represent the degree of demyelination in lesioned and non-lesioned tissue. The ratio of T2 and T1 scans also provides a quantitative metric in normal appearing CNS tissue that indicates the degree of demelyelination and axonal injury inherent in T1 and T2 relaxation times.
Myeloarchitectural features have been visualized using MRI in humans, including the stria of Gennari in V1 (Clark et al., 1992; Barbier et al., 2002; Walters et al., 2003; Bridge et al., 2005; Clare and Bridge, 2005; Eickhoff et al., 2005b; Walters et al., 2007) and tripartite lamination of area 4 (Kim et al., 2009). Other studies have shown regional differences in T1 or T1w image intensity in cortical grey matter, including differences between association cortices and primary sensory and motor cortices using surface (Fischl et al., 2004; Salat et al., 2009) and volume analyses (Steen et al., 2000). Several studies have directly compared MR images to myelin-stained sections of the same tissue. In marmosets, this approach revealed a strong correlation between T1 and T1w intensities and histologically measured myelin content and enabled accurate delineation of several cortical areas (Bock et al., 2009). In humans, a similar approach demonstrated a myeloarchitectonic difference between areas 4 and 3a in ex vivo T1 slices and myelin stained sections (Geyer et al., 2011). Also, fibers of the perforant path are visible in both T2*-weighted images and in myelin stained sections (Augustinack et al., 2010). The myelin-related MR contrast largely reflects differences in lipids (Koenig, 1991) and free and myelin-bound water (Miot-Noirault et al., 1997) concentration, but is also influenced by iron, particularly in T2*-weighted images. However, myelin and iron are strongly colocalized within cortical grey matter (Fukunaga et al., 2010). Thus, it is reasonable to conclude that MR-based signals across the cortical grey matter largely reflect myelin content both directly and indirectly. Sigalovsky et al. (2006) found an increased R1 signal (the inverse of T1) in the posterior medial Heschl's gyms and suggested that this reflected the high myelin content of primary auditory cortex. Yoshiura et al. (2000) reported that Heschl's gyms, particularly the posterior portion, has a lower T2w intensity than the superior or middle temporal gyri. These studies suggest that the myelin content of a cortical area covaries with both T1w intensity and T2w intensity, but in opposite directions.